Nuclear nano-morphology markers of histologically normal cells detect the "field effect" of breast cancer

Abstract

Accurate detection of breast malignancy from histologically normal cells ("field effect") has significant clinical implications in a broad base of breast cancer management, such as high-risk lesion management, personalized risk assessment, breast tumor recurrence, and tumor margin management. More accurate and clinically applicable tools to detect markers characteristic of breast cancer "field effect" that are able to guide the clinical management are urgently needed. We have recently developed a novel optical microscope, spatial-domain low-coherence quantitative phase microscopy, which extracts the nanoscale structural characteristics of cell nuclei (i.e., nuclear nano-morphology markers), using standard histology slides. In this proof-of-concept study, we present the use of these highly sensitive nuclear nano-morphology markers to identify breast malignancy from histologically normal cells. We investigated the nano-morphology markers from 154 patients with a broad spectrum of breast pathology entities, including normal breast tissue, non-proliferative benign lesions, proliferative lesions (without and with atypia), "malignant-adjacent" normal tissue, and invasive carcinoma. Our results show that the nuclear nano-morphology markers of "malignant-adjacent" normal tissue can detect the presence of invasive breast carcinoma with high accuracy and do not reflect normal aging. Further, we found that a progressive change in nuclear nano-morphology markers that parallel breast cancer risk, suggesting its potential use for risk stratification. These novel nano-morphology markers that detect breast cancerous changes from nanoscale structural characteristics of histologically normal cells could potentially benefit the diagnosis, risk assessment, prognosis, prevention, and treatment of breast cancer.

Fig. 1

Representative conventional images of breast biopsies and structure-derived OPD map from a single cell nucleus. a Wide field histology images, b high-magnification histology images of breast tissue biopsies, and c corresponding OPD maps of the cell nuclei (marked in circles) from (I) normal cells from a healthy patient (Category 1); (II) cells labeled as fibrocystic changes from a non-proliferative benign patient (Category 2); (III) cells labeled as ductal epithelial hyperplasia from a patient with concurrent apocrine metaplasia and cystic changes (Category 3); (IV) cells labeled as atypical lobular hyperplasia (Category 4); (V) cells labeled by the expert breast pathologist as “normal” from a patient with invasive breast carcinoma (Category 5, “malignant-adjacent” normal); and (VI) cells labeled as “malignant” from a patient with invasive breast carcinoma (Category 6). Scale bar in the image indicates 10 μm. The color bar represents the OPD value from the cell nucleus

Fig. 2

Box-and-whisker plots showing the nuclear nano-morphology markers for healthy patients with normal cells (Category 1), invasive cancer patients with “malignant-adjacent” normal cells (Category 5), and invasive cancer patients with malignant cells (Category 6): a average nuclear OPD ((〈OPD〉)_p), b intra-nuclear standard deviation ((σ_OPD)_p). For each patient, we used the mean value of 〈OPD〉 and σ_OPD by averaging ~40–60 cell nuclei

Fig. 3

Box-and-whisker plots showing the changes in nuclear nano-morphology markers from all 6 categories with a broad range of breast pathology entities: a average nuclear OPD ((〈OPD〉)_p) and b intra-nuclear standard deviation of OPD ((σ_OPD)_p). For each patient, we used the mean value of 〈OPD〉 and σ_OPD by averaging ~40–60 cell nuclei. If P value is 0.1 or larger, “NS” is used

Fig. 4

Box-and-whisker plots showing the average nuclear area from the 6 categories of breast pathology entities. The average nuclear area of normal cells and “malignant-adjacent” normal cells are not statistically significant (P = 0.2). If P value is 0.1 or larger, “NS” is used

Fig. 5

Statistical analysis of nuclear nano-morphology markers in age-matched patient groups: a 39–49-year-old patients and b 50–60-year-old patients. The non-cancerous group includes Categories 1–4. The error bar represents standard error

Fig. 6

Performance characteristics of nuclear nano-morphology markers described by cross-validated ROC curves to distinguish “malignant-adjacent” normal (Category 5) from normal (Category 1) and non-cancerous lesions (Categories 1–4). The discriminatory accuracy was assessed by the area under the ROC curve (AUROC)